The document discusses the use of biomarkers in personalized medicine for kidney transplantation. It describes different types of biomarkers including risk, diagnostic, prognostic, predictive, safety, pharmacodynamic, and monitoring biomarkers. Examples are given of biomarkers currently used for risk assessment, diagnosis, and prediction of outcomes. The document also outlines new biomarkers in development for improving risk assessment, diagnosis, prediction of outcomes, and monitoring of patients after transplantation. Personalized medicine using biomarkers has the potential to optimize decision making for kidney transplant recipients.
Stockholm Karolinska meeting: Graft histology - a marker of pain and sufferin...Maarten Naesens
In this presentation, I discuss the role for protocol kidney allograft biopsies and biopsies for cause, as opportunity for individualised immunosuppressive regimen and use of targeted therapeutic strategies, in order to prevent chronic allograft dysfunction and improve long-term graft outcome. I discuss how kidney transplant histology is re-emerging as the clinical key parameter for the fate of the graft, and display long-term implications of histological alterations. I finally discuss the value of histology as a surrogate study endpoint, and reiterate the urgent need to identify appropriate surrogate endpoints to improve long-term outcomes.
Banff 2017 meeting presentation - early versus late inflammationMaarten Naesens
My presentation at the Banff 2017 meeting in Barcelona on kidney transplant pathology on the impact of time after transplantation on transplant outcome, and the difference between diagnostic and prognostic use of the Banff scheme for allograft histopathology.
New Data on Resistance to DAAs and Implications for Therapy.2015hivlifeinfo
In this downloadable slideset, Nezam H. Afdhal, MD, FRCPI, provides guidance on testing for and management of resistance in HCV-infected patients treated with DAA therapy.
Format: Microsoft PowerPoint (.ppt)
File size: 1.39 MB
Date posted: 10/30/2015
The detrimental effects of Donor Specific HLA alloantibodies (DSA) on outcomes following liver organ transplantation have been known for many years.
Liver transplantation is an exception but some evidence has been recently highlighted, showing that DSA could be associated with acute antibody-mediated rejection, chronic rejection, plasma cell hepatitis, anastomotic biliary stricture, NRH, fibrosis progression... The prevalence of preformed donor specific DSA is about 20% and the incidence of de novo DSA is about 10% in Liver transplantation (LT). DSA are associated with several graft diseases, mainly AMR but diagnosis was made on histological features+/-C4d staining. De novo DSA and preformed class II DSA, especially with high MFI, seem to pejoratively influence outcomes after LT. When associated with HCV, DSA worsen fibrosis progression. Thanks to antiviral IFN-free regimen, therapeutic strategies of DSA positivity and/or AMR will not differ from HCV- recipients, but need to be evaluated in prospective studies.
Stockholm Karolinska meeting: Graft histology - a marker of pain and sufferin...Maarten Naesens
In this presentation, I discuss the role for protocol kidney allograft biopsies and biopsies for cause, as opportunity for individualised immunosuppressive regimen and use of targeted therapeutic strategies, in order to prevent chronic allograft dysfunction and improve long-term graft outcome. I discuss how kidney transplant histology is re-emerging as the clinical key parameter for the fate of the graft, and display long-term implications of histological alterations. I finally discuss the value of histology as a surrogate study endpoint, and reiterate the urgent need to identify appropriate surrogate endpoints to improve long-term outcomes.
Banff 2017 meeting presentation - early versus late inflammationMaarten Naesens
My presentation at the Banff 2017 meeting in Barcelona on kidney transplant pathology on the impact of time after transplantation on transplant outcome, and the difference between diagnostic and prognostic use of the Banff scheme for allograft histopathology.
New Data on Resistance to DAAs and Implications for Therapy.2015hivlifeinfo
In this downloadable slideset, Nezam H. Afdhal, MD, FRCPI, provides guidance on testing for and management of resistance in HCV-infected patients treated with DAA therapy.
Format: Microsoft PowerPoint (.ppt)
File size: 1.39 MB
Date posted: 10/30/2015
The detrimental effects of Donor Specific HLA alloantibodies (DSA) on outcomes following liver organ transplantation have been known for many years.
Liver transplantation is an exception but some evidence has been recently highlighted, showing that DSA could be associated with acute antibody-mediated rejection, chronic rejection, plasma cell hepatitis, anastomotic biliary stricture, NRH, fibrosis progression... The prevalence of preformed donor specific DSA is about 20% and the incidence of de novo DSA is about 10% in Liver transplantation (LT). DSA are associated with several graft diseases, mainly AMR but diagnosis was made on histological features+/-C4d staining. De novo DSA and preformed class II DSA, especially with high MFI, seem to pejoratively influence outcomes after LT. When associated with HCV, DSA worsen fibrosis progression. Thanks to antiviral IFN-free regimen, therapeutic strategies of DSA positivity and/or AMR will not differ from HCV- recipients, but need to be evaluated in prospective studies.
2018 09-20 biomarkers for post-transplant immune injuryMaarten Naesens
I discuss the paradigm of personalized (precision) medicine, and apply this to the field of kidney transplantation. I discuss risk markers, non-invasive and invasive diagnostic markers, prognostic and predictive markers.
Clinical Impact of New Data From AASLD 2015hivlifeinfo
In this downloadable slideset, David R. Nelson, MD, and Norah Terrault, MD, MPH, review key HCV studies presented at the 2015 Annual Meeting of the European Association for the Study of the Liver.
Format: Microsoft PowerPoint (.ppt)
File size: 2.19 MB
Date posted: 12/2/2015
In this downloadable slideset, expert faculty members Andrew Carr, MBBS, MD, FRACP, FRCPA; Daniel R. Kuritzkes, MD; and Ian M. Sanne, MBBCH, FCP(SA), review key studies presented at the 2016 International AIDS Conference.
Format: Microsoft PowerPoint (.ppt)
File size: 1.28 MB
Date posted: 8/5/2016
Conférence du Professeur Philippe Mathurin (Hôpital Universitaire Claure Huriez, Lille, France), Juin 2014. Le "Binge Drinking" est un des enjeux de santé publique majeur dans tous les pays occidentaux. Une augmentation de la mortalité par cirrhose alcoolique est constatée dans les pays où l'alcoolisme chronique et le Binge Drinking sont les plus répandus.
Современное лечение ВИЧ: когда начинать, чем начинать. Contemporary Managemen...hivlifeinfo
.Contemporary Management of HIV. When to Start, What to Start.2016/Современное лечение ВИЧ: когда начинать, чем начинать.
In this downloadable slideset, Daniel R. Kuritzkes, MD, and Program Director Eric S. Daar, MD review key data and optimal approaches for first-line ART with contemporary HIV regimens.
Format: Microsoft PowerPoint (.ppt)
File size: 2.53 MB
Date posted: 2/9/2016
Новые данные с конференции по ВИЧ-инфекции CROI 2017/Clinical Impact of New D...hivlifeinfo
Clinical Impact of New Data From CROI 2017
Expert faculty members Joel E. Gallant, MD, MPH, and Charles B. Hicks, MD, summarize key studies from this important annual conference.
Format: Microsoft PowerPoint (.ppt)
File size: 1.25 MB
Date posted: 3/3/2017
http://www.theheart.org/web_slides/1225049.do
A randomized double-blind, double-dummy trial on MAGELLAN (VTE Prophylaxis in Medically Ill Patients) to show noninferiority of rivaroxaban to enoxaparin at 10 days and superiority at 35 days
Современное лечение ВИЧ: новые парадигмы в АРТ / Contemporary Management of H...hivlifeinfo
Набор слайдов c рассмотрением важных вопросов об АРТ первого ряда, арв-препаратами пролонгированного действия и схемами АРТ с двумя препаратами, акцент в публикации на роль новых стратегий.
Современное лечение ВИЧ.Усилить или не усилить : преимущества и недостатки бу...hivlifeinfo
Современное лечение ВИЧ.Усилить или не усилить : преимущества и недостатки бустированных режимов АРТ / Contemporary Management of HIV.To Boost or Not to Boost-Advantages and Disadvantages of Boosted ART.2017
In this downloadable slideset, Eric S. Daar, MD, and Program Director Joseph J. Eron, Jr., MD, review advantages and disadvantages of boosted ART regimens for managing patients with HIV.
Format: Microsoft PowerPoint (.ppt)
File size: 514 KB
Date posted: 6/16/2017
David Haas, MD, professor at Vanderbilt University School of Medicine, presents "Pharmacogenomics of HIV therapy" for AIDS Clinical Rounds at UC San Diego
Clinical Impact of New HIV Data From the 2016 Comorbidities-Adverse Drug Reac...hivlifeinfo
In this downloadable slideset, expert faculty members Todd T. Brown, MD, PhD, and Jordan E. Lake, MD, MSc, review key studies presented at the 2016 Comorbidities/Adverse Drug Reactions Workshop.
Format: Microsoft PowerPoint (.ppt)
File size: 1.37 MB
Date posted: 10/14/2016
2018 09-20 biomarkers for post-transplant immune injuryMaarten Naesens
I discuss the paradigm of personalized (precision) medicine, and apply this to the field of kidney transplantation. I discuss risk markers, non-invasive and invasive diagnostic markers, prognostic and predictive markers.
Clinical Impact of New Data From AASLD 2015hivlifeinfo
In this downloadable slideset, David R. Nelson, MD, and Norah Terrault, MD, MPH, review key HCV studies presented at the 2015 Annual Meeting of the European Association for the Study of the Liver.
Format: Microsoft PowerPoint (.ppt)
File size: 2.19 MB
Date posted: 12/2/2015
In this downloadable slideset, expert faculty members Andrew Carr, MBBS, MD, FRACP, FRCPA; Daniel R. Kuritzkes, MD; and Ian M. Sanne, MBBCH, FCP(SA), review key studies presented at the 2016 International AIDS Conference.
Format: Microsoft PowerPoint (.ppt)
File size: 1.28 MB
Date posted: 8/5/2016
Conférence du Professeur Philippe Mathurin (Hôpital Universitaire Claure Huriez, Lille, France), Juin 2014. Le "Binge Drinking" est un des enjeux de santé publique majeur dans tous les pays occidentaux. Une augmentation de la mortalité par cirrhose alcoolique est constatée dans les pays où l'alcoolisme chronique et le Binge Drinking sont les plus répandus.
Современное лечение ВИЧ: когда начинать, чем начинать. Contemporary Managemen...hivlifeinfo
.Contemporary Management of HIV. When to Start, What to Start.2016/Современное лечение ВИЧ: когда начинать, чем начинать.
In this downloadable slideset, Daniel R. Kuritzkes, MD, and Program Director Eric S. Daar, MD review key data and optimal approaches for first-line ART with contemporary HIV regimens.
Format: Microsoft PowerPoint (.ppt)
File size: 2.53 MB
Date posted: 2/9/2016
Новые данные с конференции по ВИЧ-инфекции CROI 2017/Clinical Impact of New D...hivlifeinfo
Clinical Impact of New Data From CROI 2017
Expert faculty members Joel E. Gallant, MD, MPH, and Charles B. Hicks, MD, summarize key studies from this important annual conference.
Format: Microsoft PowerPoint (.ppt)
File size: 1.25 MB
Date posted: 3/3/2017
http://www.theheart.org/web_slides/1225049.do
A randomized double-blind, double-dummy trial on MAGELLAN (VTE Prophylaxis in Medically Ill Patients) to show noninferiority of rivaroxaban to enoxaparin at 10 days and superiority at 35 days
Современное лечение ВИЧ: новые парадигмы в АРТ / Contemporary Management of H...hivlifeinfo
Набор слайдов c рассмотрением важных вопросов об АРТ первого ряда, арв-препаратами пролонгированного действия и схемами АРТ с двумя препаратами, акцент в публикации на роль новых стратегий.
Современное лечение ВИЧ.Усилить или не усилить : преимущества и недостатки бу...hivlifeinfo
Современное лечение ВИЧ.Усилить или не усилить : преимущества и недостатки бустированных режимов АРТ / Contemporary Management of HIV.To Boost or Not to Boost-Advantages and Disadvantages of Boosted ART.2017
In this downloadable slideset, Eric S. Daar, MD, and Program Director Joseph J. Eron, Jr., MD, review advantages and disadvantages of boosted ART regimens for managing patients with HIV.
Format: Microsoft PowerPoint (.ppt)
File size: 514 KB
Date posted: 6/16/2017
David Haas, MD, professor at Vanderbilt University School of Medicine, presents "Pharmacogenomics of HIV therapy" for AIDS Clinical Rounds at UC San Diego
Clinical Impact of New HIV Data From the 2016 Comorbidities-Adverse Drug Reac...hivlifeinfo
In this downloadable slideset, expert faculty members Todd T. Brown, MD, PhD, and Jordan E. Lake, MD, MSc, review key studies presented at the 2016 Comorbidities/Adverse Drug Reactions Workshop.
Format: Microsoft PowerPoint (.ppt)
File size: 1.37 MB
Date posted: 10/14/2016
Screening is a process by which we identify the people who have the disease from those who don't have the disease by using specific tests.
It's helps in identifying the disease even before the pre symptomatic period.
It can eliminate the disease my early diagnosis or decrease the damage it causes to one person's health by early treatment .
Transmission risk factors, symptoms, diagnosis and treatment of hepatitis B. This is a deliberate presentation made to be easily understood by lay persons to appreciate the thinking of the doctors when it comes to treatment for hepatitis B
Evolving Switch Strategies for Virologically Suppressed HIV-Infected Patients...Hivlife Info
Доктор David A. Wohl при участии группы экспертов, рассматривает основные исследования о том, когда и как, при каких условиях переводить пациентов со стабильной супрессией ВИЧ на новые методы лечения .
Presentation at the Glomcon session of March 6th 2023 on microvascular inflammation after kidney transplantation and the potential adaptation of the Banff Classification
In this presentation, given for the ISN-TTS webinar on Antibody-Mediated Rejection after kidney transplantation, I discuss the phenotype of microcirculation inflammation/microvascular rejection/ABMRh in the absence of donor-specific HLA antibodies. Also the potential role of missing self activation of natural killer (NK) cells and non-HLA antibodies.
This is the presentation that I gave in Genua, which discusses the recente studies outlining the prevalence, impact, potential causes and diagnostic features of microvascular rejection after kidney transplantation, when no HLA-DSA are present.
It provides some background literature and insights for discussions on potential updates of the Banff classification of kidney transplant pathology
HLA antistoffen en antistof-gemedieerde rejectie zijn de belangrijkste oorzaken van het falen van transplantnieren. In deze presentatie wordt een moeilijk onderwerp eenvoudig uitgelegd.
2014 06-05 Pretransplant Evaluation for Kidney Transplantation - Pretransplan...Maarten Naesens
Short overview of evidence-based decisions for the pre transplant evaluation of kidney transplant recipients. Pretransplantbilan onderzoeken niertransplantatie UZ Leuven.
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
share - Lions, tigers, AI and health misinformation, oh my!.pptxTina Purnat
• Pitfalls and pivots needed to use AI effectively in public health
• Evidence-based strategies to address health misinformation effectively
• Building trust with communities online and offline
• Equipping health professionals to address questions, concerns and health misinformation
• Assessing risk and mitigating harm from adverse health narratives in communities, health workforce and health system
DISSERTATION on NEW DRUG DISCOVERY AND DEVELOPMENT STAGES OF DRUG DISCOVERYNEHA GUPTA
The process of drug discovery and development is a complex and multi-step endeavor aimed at bringing new pharmaceutical drugs to market. It begins with identifying and validating a biological target, such as a protein, gene, or RNA, that is associated with a disease. This step involves understanding the target's role in the disease and confirming that modulating it can have therapeutic effects. The next stage, hit identification, employs high-throughput screening (HTS) and other methods to find compounds that interact with the target. Computational techniques may also be used to identify potential hits from large compound libraries.
Following hit identification, the hits are optimized to improve their efficacy, selectivity, and pharmacokinetic properties, resulting in lead compounds. These leads undergo further refinement to enhance their potency, reduce toxicity, and improve drug-like characteristics, creating drug candidates suitable for preclinical testing. In the preclinical development phase, drug candidates are tested in vitro (in cell cultures) and in vivo (in animal models) to evaluate their safety, efficacy, pharmacokinetics, and pharmacodynamics. Toxicology studies are conducted to assess potential risks.
Before clinical trials can begin, an Investigational New Drug (IND) application must be submitted to regulatory authorities. This application includes data from preclinical studies and plans for clinical trials. Clinical development involves human trials in three phases: Phase I tests the drug's safety and dosage in a small group of healthy volunteers, Phase II assesses the drug's efficacy and side effects in a larger group of patients with the target disease, and Phase III confirms the drug's efficacy and monitors adverse reactions in a large population, often compared to existing treatments.
After successful clinical trials, a New Drug Application (NDA) is submitted to regulatory authorities for approval, including all data from preclinical and clinical studies, as well as proposed labeling and manufacturing information. Regulatory authorities then review the NDA to ensure the drug is safe, effective, and of high quality, potentially requiring additional studies. Finally, after a drug is approved and marketed, it undergoes post-marketing surveillance, which includes continuous monitoring for long-term safety and effectiveness, pharmacovigilance, and reporting of any adverse effects.
263778731218 Abortion Clinic /Pills In Harare ,sisternakatoto
263778731218 Abortion Clinic /Pills In Harare ,ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group ABORTION WOMEN’S CLINIC +27730423979 IN women clinic we believe that every woman should be able to make choices in her pregnancy. Our job is to provide compassionate care, safety,affordable and confidential services. That’s why we have won the trust from all generations of women all over the world. we use non surgical method(Abortion pills) to terminate…Dr.LISA +27730423979women Clinic is committed to providing the highest quality of obstetrical and gynecological care to women of all ages. Our dedicated staff aim to treat each patient and her health concerns with compassion and respect.Our dedicated group of receptionists, nurses, and physicians have worked together as a teamof receptionists, nurses, and physicians have worked together as a team wwww.lisywomensclinic.co.za/
Adv. biopharm. APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMSAkankshaAshtankar
MIP 201T & MPH 202T
ADVANCED BIOPHARMACEUTICS & PHARMACOKINETICS : UNIT 5
APPLICATION OF PHARMACOKINETICS : TARGETED DRUG DELIVERY SYSTEMS By - AKANKSHA ASHTANKAR
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists Saeid Safari
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
NVBDCP.pptx Nation vector borne disease control programSapna Thakur
NVBDCP was launched in 2003-2004 . Vector-Borne Disease: Disease that results from an infection transmitted to humans and other animals by blood-feeding arthropods, such as mosquitoes, ticks, and fleas. Examples of vector-borne diseases include Dengue fever, West Nile Virus, Lyme disease, and malaria.
2. Personalized medicine builds on data
and biomarkers
rker
Prognostic
biomarker
Predictive
biomarker
High disease probability
se High risk / bad
prognosis
Low risk / good
prognosis
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Risk/susceptibility
biomarker
High risk for diseaseLow risk for disease
All patientsAll patients
same treatment
Biomarkers
Traditional medicine
Personalized medicine
3. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Risk/susceptibility markers
4. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Non-invasive
diagnostic marker
5. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Invasive diagnostic marker
6. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Prognostic marker
7. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Predictive marker
Predictive marker
8. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
Predictive marker
Safety markers
Pharmacodynamic markers
Monitoring markers
9. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
10. Risk/susceptibility
biomarker
Risk/susceptibility
biomarker
Non-invasive
diagnostic biomarker
Non-invasive diagnostic
biomarker
Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilityLow disease probability
High risk for diseaseLow risk for disease
No disease confirmation Disease confirmation
High risk / bad
prognosis
Low risk / good
prognosis
Safety biomarker
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Pharmacodynamic /
response biomarker
Monitoring biomarker
- Disease activity
- Pharmacokinetics
(exposure)
All patients
Patients with
confirmed disease
PERSONALIZED
MEDICINE
Risk/susceptibility markers
Non-invasive
diagnostic markers
Invasive
diagnostic markers
Prognostic markers
Predictive markers
Safety markers
Pharmacodynamic markers
Monitoring markers
13. We have several widely used susceptibility/risk biomarkers
in kidney transplantation
• Number of HLA mismatches
• Cross-matches
• Pretransplant PRA%
• Pretransplant DSA
• De novo DSA occurrence
Success story of
HLA genotyping and
antibody profiling
TRANSPLANTATION
MEDICINE
=
FRONTRUNNER IN
PERSONALIZED MEDICINE
14. More personalization in allocation
(AM program) leads to better outcome
Heidt et al confidential
15. More personalization in allocation
(AM program) leads to better outcome
Heidt et al confidential
17. HLA epitope-based organ allocation instead of waiting time
Low resolution
Acceptable MM at the antigen level
High/allelic resolution
Acceptable MM at the epitope level
18. HLA epitope-based organ allocation instead of waiting time
Third-Generation Sequencing (SMRT)
Low resolution
Acceptable MM at the antigen level
High/allelic resolution
Acceptable MM at the epitope level
19. New risk biomarkers in the pipeline
for kidney transplantation
• Epitope mismatch load1
• Genetic assessment for aHUS recurrence
• Urinary or serum suPAR for FSGS recurrence2
• FSGS recurrence panel3
• PLA2R and THSD7A antibodies for recurrence of membranous
glomerulopathy4,5
• Donor-reactive T-cell response6
• …
Risk markers
1Wiebe et al Transplantation 2016; 2Franco Palacios et al Transplantation 2013; 3Delville et al Sci Transl Med 2014;
4Sprangers et al Transplant Rev 2013; 5Tomas et al J Clin Invest 2016; 6Crespo et al Clin Biochem 2016
21. We have several widely used diagnostic biomarkers
in kidney transplantation
Non-invasive:
• Serum creatinine/eGFR
• Proteinuria
• DSAs
• Renal ultrasound exam
Invasive:
• Histology of for-cause (indication) biopsies
• Histology of protocol biopsies
22. Naesens et al. Am J Transplant 2013;13:86–99.
Inflammation + ABMR
Inflammation - ABMR
Normal
Chronic - inflammation
Chronic + inflammation
Transplant glomerulopathy
gs
cv
mm
ah
ct
ci
cg
ti
i
t
ptc
g
v
C4dglom
C4dptc
0 max
Individual lesion score
Inflammation + ABMR
Inflammation - ABMR
Normal
Chronic - inflammation
Chronic + inflammation
Transplant glomerulopathy
gs
cv
mm
ah
ct
ci
cg
ti
i
t
ptc
g
v
C4dglom
C4dptc
0 max
Individual lesion score
23. We are constantly refining the diagnostic Banff classification
1. Racusen LC et al. Kidney Int 1999;55:713–723;
2. Loupy A et al. Am J Transplant 2017;17(1):28–41.
ABMR
2015
TCMR
1997–2015
24. Lesions are non-specific for the underlying etiology:
TCMR and ABMR
Lefaucheur C et al. Lancet 2013;381:313–319.
25. Kidney transplant histology is highly problematic
as a diagnostic biomarker
Naesens & Anglicheau – in press.
26. Loupy et al AJT 2017
BANFF 2015 consensus
Invasive diagnostic markers
in the pipeline for kidney transplantation
27. From MMDx website: www.molecular-microscope.com
Invasive diagnostic markers
in the pipeline for kidney transplantation
28. Non-invasive diagnostic markers
in the pipeline for kidney transplantation
• Urinary mRNA
• Urinary miRNA
• Urinary proteins/peptides
• Blood mRNA
• Blood miRNA
• Blood proteins/peptides
• ….
29. 0 25 50 75 100
0
25
50
75
100
Sensitivity for acute rejection (%)
Specifictyforacuterejection(%)
Perforin
Granzyme B
PI-9
CD103
FOXP3
CXCL10
NKG2D
TIM3
Granulysin
Multigene signature
Urinary mRNA
0 25 50 75 100
0
Sensitivity for acute rejection (%)
Multigene signature
Non-invasive urinary mRNA markers
in the pipeline for kidney transplantation lack accuracy
Naesens and Anglicheau, in press
mRNA
30. 0 25 50 75 100
0
25
50
75
100
Sensitivity for acute rejection (%)
Specifictyforacuterejection(%)
Perforin
Granzyme B
PI-9
CD103
FOXP3
CXCL10
NKG2D
TIM3
Granulysin
Multigene signature
Urinary mRNA
0 25 50 75 100
0
Sensitivity for acute rejection (%)
Multigene signature
Non-invasive urinary mRNA markers
in the pipeline for kidney transplantation lack accuracy
mRNA
Naesens and Anglicheau, in press
31. 0 25 50 75 100
0
Sensitivity for acute rejection (%)
Multigene signature
0 25 50 75 100
0
25
50
75
100
Sensitivity for acute rejection (%)
Specifictyforacuterejection(%)
CXCL9
CXCL10
Fractalkine
Urinary proteins
Non-invasive urinary protein markers
in the pipeline for kidney transplantation lack accuracy
Proteins
Naesens and Anglicheau, in press
32. 0 25 50 75 100
0
25
50
75
100
Sensitivity for acute rejection (%)
Specifictyforacuterejection(%)
Blood mRNA
Granzyme B
Perforin
FasL
HLA-DRA
Multigene signature
Non-invasive blood mRNA markers
in the pipeline for kidney transplantation seem promising
Naesens and Anglicheau, in press
mRNA
33. 0 25 50 75 100
0
25
50
75
100
Sensitivity for acute rejection (%)
Specifictyforacuterejection(%)
Blood mRNA
Granzyme B
Perforin
FasL
HLA-DRA
Multigene signature
Non-invasive blood mRNA markers
in the pipeline for kidney transplantation seem promising
Trugraf
kSORT
Naesens and Anglicheau, in press
34. The kSORT assay needs further validation
in cross-sectional cohorts
17 peripheral blood mRNA gene-set
Case-control setting -> PPV?? NPV??
Roedder et al PLOS Med 2014
35. The TruGraf assay needs further validation
200 peripheral blood mRNA geneset
- early-access clinical programs started
- large interventional trials ongoing (like the phase-3 trial with the p53 inhibitor QPI-1002)
- prospective, randomized, multi-center clinical trial ongoing
Kurian et al Am J Transplant 2014
36. AP-HP Paris
CHU Limoges
UZ Leuven
MHH Hannover
Clinical Centers
AP-HP Paris
INSERM Limoges
KU Leuven
Mosaiques Diagnostic GmbH
Analytical Centers (-omics data)
INSERM Toulouse
CEA
CNRS
VITO
Bio-informatics Center
Acureomics
UnivPDes
Inserm-Transfert
Coordination
Cardinal Systems
Urinary + plasma metabolomics
Urinary proteomics
+ peptidomics
Urinary miRNA
Urinary mRNA
Blood + biopsy miRNA
Blood + biopsy mRNA
Biopsy lipidomics, peptidomics, proteomics
Urinary proteomics and peptidomics
Blood + biopsy miRNA
Urinary lipidomics
Urinary proteomics
+ peptidomics
www.biomargin.eu
38. Accuracy of a test determines its clinical value,
not its p-value!
Area under a
ROC curve
Interpretation
0.90 – 1.00 Excellent
0.80 – 0.90 Good
0.70 – 0.80 Fair
0.60 – 0.70 Poor
0.50 – 0.60 Fail
False positive rate (1 – Specificity)
Truepositiverate(Sensitivity)
Perfect test
AUC=1.00
Good test
AUC=0.85
Failed test
AUC=0.50
Positive predictive value (PPV) and negative predictive
value (NPV) take disease prevalence into account
39. eGFR at 1 year is associated with graft outcome,
and is a fair prognostic marker
ROC for graft failure
5 year after biopsy
according to 1 year MDRD eGFR
0 20 40 60 80 100
0
20
40
60
80
100
100% - Specificity%
Sensitivity%
AUC=0.77
p<0.0001
MRDR eGFR at 1 year
and graft failure
1 5 10 15
0
20
40
60
80
100
Time after biopsy (years)
Graftsurvival(%)
>70 mL/min
60-70 mL/min
50-60 mL/min
log-rank
P<0.0001
40-50 mL/min
30-40 mL/min
20-30 mL/min
<20 mL/min
Speaker’s own unpublished data
40. Proteinuria is a risk factor for graft failure
but a poor prognostic marker
Naesens M et al J Am Soc Nephrol 20153 months 1 year 2 yearsD
Time after transplantation (years)
572
119
40
430
68
16
163
23
7
532
102
33
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
>1.0 g/24h
Time after transplantation (yea
495
104
38
416
70
13
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
>1.0 g/24h
Biopsy time points
(N=1335)
1 5 10
0
20
40
60
80
100
Time after biopsy (years)
Percentsurvival
>3.0 g/24h
< 0.3 g/24h
0.3-1.0 g/24h
1.0-3.0 g/24h
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
1.0-3.0 g/24h
>3.0 g/24h
548
319
150
30
449
237
94
18
297
156
53
11
646
390
208
51
log-rank
P <0.0001
Proteinuria
B C
0 20 40 60 80 100
0
20
40
60
80
100
False Positive Fraction (%)
TruePositiveFraction(%)
AUC=0.66
(95% CI 0.63-0.69)
P <0.0001
Biopsy time points
(N=1335)
3 months
(N=914)
100
)
1 year
(N=778)
100
)
2 years
(N=731)
100
)
)
D
572
119
40
430
68
16
163
23
7
532
102
33
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
>1.0 g/24h
495
104
38
160
28
7
416
70
13
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
>1.0 g/24h
Biopsy time points
(N=1335)
1 5 10
0
20
40
60
80
100
Time after biopsy (years)
Percentsurvival
>3.0 g/24h
< 0.3 g/24h
0.3-1.0 g/24h
1.0-3.0 g/24h
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
1.0-3.0 g/24h
>3.0 g/24h
548
319
150
30
449
237
94
18
297
156
53
11
646
390
208
51
log-rank
P <0.0001
Proteinuria
B C
0 20 40 60 80 100
0
20
40
60
80
100
False Positive Fraction (%)
TruePositiveFraction(%)
AUC=0.66
(95% CI 0.63-0.69)
P <0.0001
Biopsy time points
(N=1335)
5 10 152
0
20
40
60
80
Time after transplantation (years)
0.3-1.0 g/24h
> 1.0 g/24h
572
119
40
430
68
16
163
23
7
532
102
33
log-rank
P <0.0001
at risk
g/24h
g/24h
g/24h
5 10
0
20
40
60
80
Time after transplantation (y
Percentsurviv
495
104
38
416
70
13
log-rank
P <0.0001
No. at risk
<0.3 g/24h
0.3-1.0 g/24h
>1.0 g/24h
Biopsy time points
(N=1335)
60
80
100
>3.0 g/24h
< 0.3 g/24h
0.3-1.0 g/24h
1.0-3.0 g/24h
Proteinuria
C
60
80
100
Fraction(%)
Biopsy time points
(N=1335)
41. Starzl TE et al Ann Surg 1974;180(4):606–614
ct ci ah cvi
64 cases transplanted between 1962–1964
in Colorado and Denver
42. The CADI score is an imperfect prognostic marker,
despite the significant association with graft failure
CADI score
in indication biopsy
1 5 10 15
0
20
40
60
80
100
Time after biopsy (years)
Graftsurvival(%)
CADI 0
CADI 1
CADI 2-3
log-rank
P<0.0001
CADI 4-5
CADI 6-7
CADI 8-9
CADI >9
ROC for graft failure
5 year after biopsy
according to CADI score
0 20 40 60 80 100
0
20
40
60
80
100
100% - Specificity%
Sensitivity%
AUC=0.65
p<0.0001
CADI score
in indication biopsy
1 5 10 15
0
20
40
60
80
100
Time after biopsy (years)
Graftsurvival(%)
CADI 0
CADI 1
CADI 2-3
log-rank
P<0.0001
CADI 4-5
CADI 6-7
CADI 8-9
CADI >9
ROC for graft failure
5 year after biopsy
according to CADI score
0 20 40 60 80 100
0
20
40
60
80
100
100% - Specificity%
Sensitivity%
AUC=0.65
p<0.0001
ROC for 5 year graft loss
N=1335 indication biopsies
Speaker’s own unpublished data
43. Prognostic models within a disease phenotype show
which patients need treatment
Loupy A et al. J Am Soc Nephrol 2015;26(7):1721–1731.
44. Prognostic models within a disease phenotype show
which patients need treatment
50%
Loupy A et al. J Am Soc Nephrol 2015;26(7):1721–1731.
45. We lack good prognostic biomarkers in
kidney transplantation
• eGFR
• Proteinuria
• Histology
have on itself insufficient prognostic capacity
In addition, and even more importantly, these markers reflect primarily
past injury, and not future/ongoing injury
We do not identify those the patients that need treatment
46. Adapted from Naesens et al.
J Am Soc Nephrol 2016;27(1):281–292.
0.3-1.0 vs. <0.3 g/24h
1.0-3.0 vs. <0.3 g/24h
>3.0 vs. <0.3 g/24h
30-45 vs. >45 mL/min/m2
15-30 vs. >45 mL/min/m2
<15 vs. >45 mL/min/m2
g+ptc ≥2 vs. <2
Banff grade 1 vs. 0
Banff grade 2-3 vs. 0
Banff grade 1 vs. 0
Banff grade 2-3 vs. 0
Present vs. absent
Present vs. absent
0.1 1 10 100
Proteinuria
eGFR
IFTA
Transplant
glomerulopathy
GNF
PVAN
microcirc. inflammation
Hazard ratio (95% CI)
for kidney graft loss
Prognostic model?
Several prognostic markers are independent risk factors
for graft failure
Decide who to treat
Surrogate endpoint
47. iBox provides a prognostic nomogram,
but is not (yet) disease-specific
Loupy, Aubert, Orandi, Naesens et al submitted
48. iBox provides a prognostic nomogram,
but is not (yet) disease-specific
ROC-AUC = 0.81-0.84
Loupy, Aubert, Orandi, Naesens et al submitted
49. Prognostic markers
in the pipeline for kidney transplantation
• Edmonton classifier for graft loss1
• Edmonton “ABMR molecular score”2
• GOCAR 13-geneset3
1Einecke et al J Clin Invest 2010; 2Loupy et al JASN 2013; 3O’Connell Lancet 2016
50. Molecular “Risk score” predicts graft outcome
better than histology or proteinuria
Low risk score
High risk score
Time after biopsy
Survivalprobability
Einecke et al J Clin Invest 2010
AUC=0.83
Risk score for graft loss:
Early biopsies:
Sensitivity = 100%
Specificity = 41%
PPV = 5%
NPV = 100%
Late biopsies:
Sensitivity = 83%
Specificity = 63%
PPV = 47%
NPV = 90%
51. INTERCOM STUDY
(multicenter)
ABMR Score -
Histology -
ABMR Score -
Histology +
ABMR Score +
Histology +
ABMR Score +
Histology -
Halloran et al Am J Transplant 2013
ABMR score for graft loss:
Sensitivity = 75%
Specificity = 81%
PPV = 48%
NPV = 93%
ROC AUC=0.81
Molecular “ABMR score” predicts graft outcome
better than histology of ABMR
52. “GoCAR 13-gene score” predicts CADI
better than clinical and pathological parameters
O’Connell, Zhang et al Lancet 2016
53. GoCAR score for graft loss:
PPV = ???
NPV = ???
ROC AUC=0.84
“GoCAR 13-gene score” predicts graft failure
better than clinical and pathological parameters
O’Connell, Zhang et al Lancet 2016
58. y
Non-invasive
diagnostic biomarker
stic Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilitylity
High risk for disease High risk / bad
prognosis
Low risk / good
prognosis
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Patients with
confirmed disease
Prognostic biomarker
Predictive biomarker
59. Prognostic biomarker
Predictive biomarker
y
Non-invasive
diagnostic biomarker
stic Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilitylity
High risk for disease High risk / bad
prognosis
Low risk / good
prognosis
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Patients with
confirmed disease
60. Prognostic biomarker
Predictive biomarker
y
Non-invasive
diagnostic biomarker
stic Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilitylity
High risk for disease High risk / bad
prognosis
Low risk / good
prognosis
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Patients with
confirmed disease
61. Prognostic biomarker
Predictive biomarker
y
Non-invasive
diagnostic biomarker
stic Invasive diagnostic
biomarker
Prognostic
biomarker
Predictive
biomarker
High disease probabilitylity
High risk for disease High risk / bad
prognosis
Low risk / good
prognosis
Predicted benefit from
treatment X, not Y
Predicted benefit from
treatment Y, not X
Start treatment X Start treatment Y
Patients with
confirmed disease
?
62. We urgently need new therapies for the high-risk patients,
through awareness and renewed investment
1 2 3 40 5
0
50
100
Years after transplantation
Survivalprobability(%)
(death-censored)
Transplanted in UZ Leuven
01/01/2011 - 01/09/2016
No HLAAbs (N=508)
Non-DSA HLAAbs (N=110)
DSA (N=58)
Log-rank P = 1.9E-05
1 2 3 40 5
0
50
100
Years after transplantation
Survivalprobability(%)
(overallgraftsurvival)
Transplanted in UZ Leuven
01/01/2011 - 01/09/2016
No HLAAbs (N=508)
Non-DSA HLAAbs (N=110)
DSA (N=58)
Log-rank P = 3.9E-03
Naesens et al - unpublished
Dear colleagues, our profession is changing rapidly. I am a nephrologist, taking care of kidney transplant patients, and together with all of you, I am witnessing one of the major paradigm shift in the history of medicine. While in traditional medicine, we have treated our patients according to what we think is best for the patient population as a whole, we are now moving towards personalized medicine, where we provide specific treatment X to individual patients, and treatment Y to other individuals. This personalized or individualized treatment builds on data, often big data, and biomarkers. This is what this presentation will be about. I will guide you through how I see kidney transplant pathology has a place in this transition of traditional to personalized.
When we discuss biomarkers, we should be very careful. There are many different types of biomarkers, and each of them has a specific place in personalization of medicine.
Why personalized medicine? This buzz-word is used all the time, but clearly illustrates a crucial aspect of medicine, and the changing paradigms in our profession.
In more traditional medicine, think of e.g. transplantation, we treat all the patients the same way, and hope that the treatment will be beneficial for the group of patients.
In personalized medicine, we are trying to pick those patients from the population that benefit from the treatment, and also identify which patients need which treatment to get better outcome.
And for that, we need patient data, often big data, and biomarkers.
When I say biomarkers, it is becoming obvious that there are many different types of biomarkers, and that we need to be clear on these different biomarker types.
It is important to clearly identify the different types of biomarkers, as this is crucial in understanding how we can use the available biomarkers for real clinical benefit.
Risk markers are very important, as they provide us tools to know which patients need extra attention, to be maximally efficient, and not waste time and money to patients who have no risk for disease.
In high-risk patients, you need to know the timing when disease processes start, preferably at the subclinical level. This search for early disease manifestations typically needs repeated assessment, and should thus be done with non-invasive markers,; eg in blood samples, urine samples, ultrasound examinations etc. Invasive markers cannot be repeated too often, because of their invasiveness and thus risk of side effects of the monitoring on itself. Something we of course need to avoid at all times. Non-invasive markers provide you the probability of active or beginning disease.
Very often, the disease is then confirmed and the exact phenotype is often determined based on invasive markers, like biopsies.
Disease confirmation, but also phenotypic classification, diagnostic fine-tuning
Once the disease/diagnosis is confirmed, you cannot yet start treatment. Ideally, first it is necessary to know which patients will cure even without treatment, and which patients have bad prognosis if not treated. We need to know for which patients treatment is necessary, and in which patients treatment and treatment-associated side efffects and costs can be avoided. This is done with prognostic biomarkers.
And it is only the integration of all these different markers that brings us the potential of true personalized medicine.
.
Only with this integration of many different types of biomarkers, we can come to true personalized medicine.
Urinary 3-gene mRNA expression signature, and wide range of other suggested molecules 3, 44
Wide range of urinary target proteins like CXCL10 and CXCL9 3
Blood 17 gene mRNA expression “kSORT™” 47
Blood 200-gene mRNA expression (“Trugraf”) 46
Several blood and urine miRNAs 3
Molecular microscope for allograft pathology
The problem is to define how we can use this to improve on the gold standard, which is histology.
Urinary 3-gene mRNA expression signature, and wide range of other suggested molecules 3, 44
Wide range of urinary target proteins like CXCL10 and CXCL9 3
Blood 17 gene mRNA expression “kSORT™” 47
Blood 200-gene mRNA expression (“Trugraf”) 46
Several blood and urine miRNAs 3
Molecular microscope for allograft pathology
Once a diagnosis is made, it is clear that we then need to identify which patients or diseases need treatment, and which not. No all rejections are deleterious for outcome, not all histological changes need treatment.
And this brings us to a crucial issue in biomarker research. IT IS NOT THE P-VALUES THAT COUNT, BUT THE ACCURACY OF THE MARKER.
The fact that histology is an ideal instrument for evaluating kidney allograft prognosis was already evident for Tom Starzl and his team, in the publication they made of the first 64 cases of succesful kidney transplants wordlwide. They performed protocol-specified biopsies at 2 years after transplantation, and discovered that several biopsies had chronic injury, as is illustrated in this slide.
You cannot use on-of phenomena (like ABMR present vs. absent) to calculate a prognostic value.
The accuracy of the diagnosis of ABMR for prediction of survival after 8 years is very low: 50% chance to still have your graft, 50 % chance of graft loss.
This risk score was calculated on only the late biopsies!
NB. The predictive performance was explicitly mentioned in the manuscript
Predictive performance (apart from ROC AUC) was NOT mentioned in the manuscript, but could be deducted
ABMR score for graft loss:
Sensitivity = 24/32 = 75%
Specificity = 33/293 = 81%
PPV = 24/50 = 48%
NPV = 108/116 = 93%
ROC AUC=0.81
This study is on late ABMR, and perhaps not representative of early ABMR? (the prevalence of early ABMR in these low-risk cohorts is less than 2% of biopsies with molecular assessment). In addition, these indications biopsies had lots of chronic injury, concomitant diseases etc.
As in cancer, homogenous and well phenotyped cohorts are needed to ascertain whether the molecularmicroscope strategy could be helpful and add to the conventional assessments.
Interesting : both histology of TCMR and TCMR molecular microscope score were not associated with graft survival.
This risk score was calculated on only the late biopsies!
NB. The predictive performance was explicitly mentioned in the manuscript
This risk score was calculated on only the late biopsies!
NB. The predictive performance was explicitly mentioned in the manuscript
Problem: very few novel drugs developed in transplantatation. Predictive marker development for prediction of novel treatment success therefore severely hampered.
Problem: very few novel drugs developed in transplantatation. Predictive marker development for prediction of novel treatment success therefore severely hampered.